Systems and methods for recommending an estimated time of arrival

ABSTRACT

The present disclosure relates to systems and methods for determining an estimated time of arrival (ETA) for a route, based on a model of ETA. The systems may perform the methods to obtain a first electrical signal associated with at least one route having at least one road section; generate and save first structured data of at least one global feature vector and at least one historical duration associated with the at least one route based on the first electrical signal; generate second structured data of a model of ETA by training the model based on the at least one global feature vector and the at least one historical duration; and save the second structured data of the model of ETA in the at least one non-transitory computer-readable storage medium.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of International Application No.PCT/CN2017/080850, filed on Apr. 18, 2017, which designates the UnitedStates of America and claims priority to Chinese Application No.201610242067.5, filed on Apr. 18, 2016, the contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods fordetermining an estimated time of arrival (ETA) for a route, and inparticular, systems and methods for determining the ETA using a model ofETA.

BACKGROUND

With the development of Internet technology, on-demand services, such asonline taxi hailing services and delivery services, play a significantrole in people's daily life. For example, on-demand transportationservice for a transportation may heavily be used by a user (e.g., apassenger). Through an online on-demand service platform, the user mayrequest an on-demand service in the form of an on-demand service throughan application installed in a user equipment, such as a smartphoneterminal.

SUMMARY

According to an aspect of the present disclosure, a system may includeat least one non-transitory computer-readable storage medium and atleast one processor of an online on-demand service platform from arequestor terminal configured to communicate with the at least onenon-transitory computer-readable storage medium. The at least onenon-transitory computer-readable storage medium may include a set ofinstructions for determining an estimated time of arrival (ETA) for anintended route. When the at least one processor executing the set ofinstructions, the at least one processor may be directed to perform oneor more of the following operations. The at least one processor mayobtain a first electrical signal associated with at least one routehaving at least one road section. The at least one processor maygenerate and save first structured data of at least one global featurevector and at least one historical duration associated with the at leastone route based on the first electrical signal. The at least oneprocessor may generate second structured data of a model of ETA bytraining the model based on the at least one global feature vector andthe at least one historical duration. The at least one processor maysave the second structured data of the model of ETA in the at least onenon-transitory computer-readable storage medium.

According to a further aspect of the present disclosure, a method mayinclude one or more of the following operations. At least one computerserver of an online on-demand service platform from a requestor terminalmay obtain a first electrical signal associated with at least one routehaving at least one road section. The at least one computer server maygenerate and save first structured data of at least one global featurevector and at least one historical duration associated with the at leastone route based on the first electrical signal. The at least onecomputer server may generate second structured data of a model of ETA bytraining the model based on the at least one global feature vector andthe at least one historical duration. The at least one computer servermay save the second structured data of the model of ETA in at least onenon-transitory computer-readable storage medium.

According to another aspect of the present disclosure, a non-transitorymachine-readable storage medium may include instructions. When thenon-transitory machine-readable storage medium accessed by at least oneprocessor of an online on-demand service platform from a requestorterminal, the instructions may cause the at least one processor toperform one or more of the following operations. The instructions maycause the at least one processor to obtain a request of an on-demandservice including a current default service location through a wirelessnetwork. The instructions may cause the at least one processor to obtaina first electrical signal associated with at least one route having atleast one road section. The instructions may cause the at least oneprocessor to generate and save first structured data of at least oneglobal feature vector and at least one historical duration associatedwith the at least one route based on the first electrical signal. Theinstructions may cause the at least one processor to generate secondstructured data of a model of ETA by training the model based on the atleast one global feature vector and the at least one historicalduration. The instructions may cause the at least one processor to savethe second structured data of the model of ETA in the non-transitorycomputer-readable storage medium.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a block diagram illustrating an exemplary on-demand servicesystem according to some embodiments of the present disclosure;

FIG. 2 is a block diagram illustrating an exemplary processing engineaccording to some embodiments of the present disclosure;

FIG. 3 is a flowchart illustrating an exemplary process and/or methodfor determining an ETA for a transportation service according to someembodiments of the present disclosure;

FIG. 4 is a flowchart illustrating an exemplary process and/or methodfor determining an ETA for an intended route according to someembodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process and/or methodfor determining an ETA based on a sub-model of ETA according to someembodiments of the present disclosure;

FIG. 6 illustrates exemplary diagrams of a table associated with globalfeature vector according to some embodiments of the present disclosure;

FIG. 7 illustrates exemplary diagrams of a decision tree as model of ETAaccording to some embodiments of the present disclosure;

FIG. 8 illustrates exemplary diagrams of an artificial neural network asmodel of ETA according to some embodiments of the present disclosure;

FIG. 9 illustrates exemplary diagrams of a physical model for predictingan ETA according to some embodiments of the present disclosure;

FIG. 10 is a schematic diagram illustrating exemplary hardware and/orsoftware components of a computing device according to some embodimentsof the present disclosure; and

FIG. 11 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which a userterminal may be implemented according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

The following description is presented to enable any person skilled inthe art to make and use the present disclosure, and is provided in thecontext of a particular application and its requirements. Variousmodifications to the disclosed embodiments will be readily apparent tothose skilled in the art, and the general principles defined herein maybe applied to other embodiments and applications without departing fromthe spirit and scope of the present disclosure. Thus, the presentdisclosure is not limited to the embodiments shown, but is to beaccorded the widest scope consistent with the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments in the presentdisclosure. It is to be expressly understood, the operations of theflowchart may be implemented not in order. Conversely, the operationsmay be implemented in inverted order, or simultaneously. Moreover, oneor more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

Moreover, while the system and method in the present disclosure isdescribed primarily in regard to distributing a request for atransportation service, it should also be understood that the presentdisclosure is not intended to be limiting. The system or method of thepresent disclosure may be applied to any other kind of on demandservice. For example, the system or method of the present disclosure maybe applied to transportation systems of different environments includingland, ocean, aerospace, or the like, or any combination thereof. Thevehicle of the transportation systems may include a taxi, a private car,a hitch, a bus, a train, a bullet train, a high speed rail, a subway, avessel, an aircraft, a spaceship, a hot-air balloon, a driverlessvehicle, or the like, or any combination thereof. The transportationsystem may also include any transportation system for management and/ordistribution, for example, a system for sending and/or receiving anexpress. The application of the system or method of the presentdisclosure may be implemented on a user device and include a webpage, aplug-in of a browser, a client terminal, a custom system, an internalanalysis system, an artificial intelligence robot, or the like, or anycombination thereof.

The term “passenger,” “requestor,” “service requestor,” and “customer”in the present disclosure are used interchangeably to refer to anindividual, an entity, or a tool that may request or order a service.Also, the term “driver,” “provider,” and “service provider” in thepresent disclosure are used interchangeably to refer to an individual,an entity, or a tool that may provide a service or facilitate theproviding of the service.

The term “service request,” “request for a service,” “requests,” and“order” in the present disclosure are used interchangeably to refer to arequest that may be initiated by a passenger, a service requestor, acustomer, a driver, a provider, a service provider, or the like, or anycombination thereof. The service request may be accepted by any one of apassenger, a service requestor, a customer, a driver, a provider, or aservice provider. The service request may be chargeable or free.

The term “service provider terminal” and “driver terminal” in thepresent disclosure are used interchangeably to refer to a mobileterminal that is used by a service provider to provide a service orfacilitate the providing of the service. The term “service requestorterminal” and “passenger terminal” in the present disclosure are usedinterchangeably to refer to a mobile terminal that is used by a servicerequestor to request or order a service.

The positioning technology used in the present disclosure may be basedon a global positioning system (GPS), a global navigation satellitesystem (GLONASS), a compass navigation system (COMPASS), a Galileopositioning system, a quasi-zenith satellite system (QZSS), a wirelessfidelity (WiFi) positioning technology, or the like, or any combinationthereof. One or more of the above positioning systems may be usedinterchangeably in the present disclosure.

An aspect of the present disclosure relates to online systems andmethods for determining an estimated time of arrival (ETA) for a route,based on a model of ETA. The systems and methods take a route thatincludes at least one road section as a whole, and obtain a globalfeature vector thereof. As a result, the global features no only includefeatures of each individual road sections, but also embraces interactionrelations between different road sections, adjacent or not adjacent.After obtaining the global feature vectors of a plurality of routes, thesystems and methods may train the ETA model by the global feature vectorof the plurality of routes and historical travel durations that vehiclestook to travel through the plurality of routes. The trained ETA modelthen may be used to determine ETA of an actual driver.

It should be noted that the online on-demand transportation service,such as online taxi hailing, is a newly emerged service rooted inpost-Internet era. It provides the technical solutions to the passengersand drivers that could raise in post-Internet era. In the pre-Internetera, when a passenger hails a taxi, the passenger may have no knowledgeof an estimated time of arrival to a destination or location. If thepassenger hails a taxi through a telephone call, it may be difficult forthe service provider (e.g., a driver, a taxi company, a post office, adelivery company, or an agent, etc.) to estimate a time arriving thedestination for the passenger. Online on-demand transportation system,however, may determine an estimated time of arrival for the passengerbased on a model of ETA and an intended feature vector obtaining from anintended route. By training the global feature vector and the historicalduration, the online on-demand transportation system may provide themodel of ETA or a sub-model of ETA for the intended route. A user suchas a passenger or a driver may use the model of ETA or the sub-model ofETA to predict the ETA before confirming a service request for atransportation service (e.g., hailing a taxi). The global feature vectorfor training the model of ETA or the sub-model of ETA may be obtainedfrom electrical signal associated with at least one road section.Therefore, through Internet, the online on-demand transportation systemsmay provide a much more convenient and efficient transaction platformfor the passengers and the drivers that may never be met in atraditional pre-Internet transportation service system.

FIG. 1 is a block diagram illustrating an exemplary on-demand servicesystem 100 according to some embodiments. For example, the on-demandservice system 100 may be an online transportation service platform fortransportation services. The on-demand service system 100 may include aserver 110, a network 120, a service requestor terminal 130, a serviceprovider terminal 140, a vehicle 150, a database 160, and a navigationsystem 170.

The on-demand service system 100 may provide a plurality of services.Exemplary service may include a taxi hailing service, a chauffeurservice, an express car service, a carpool service, a bus service, adriver hire service, and a shuttle service. In some embodiments, theon-demand service may be any on-line service, such as booking a meal,shopping, or the like, or any combination thereof.

In some embodiments, the server 110 may be a single server, or a servergroup. The server group may be centralized, or distributed (e.g., theserver 110 may be a distributed system). In some embodiments, the server110 may be local or remote. For example, the server 110 may accessinformation and/or data stored in the service requestor terminal 130,the service provider terminal 140, and/or the database 160 via thenetwork 120. As another example, the server 110 may be directlyconnected to the service requestor terminal 130, the service providerterminal 140, and/or the database 160 to access stored informationand/or data. In some embodiments, the server 110 may be implemented on acloud platform. Merely by way of example, the cloud platform may includea private cloud, a public cloud, a hybrid cloud, a community cloud, adistributed cloud, an inter-cloud, a multi-cloud, or the like, or anycombination thereof. In some embodiments, the server 110 may beimplemented on a computing device 1000 having one or more componentsillustrated in FIG. 10 in the present disclosure.

In some embodiments, the server 110 may include a processing engine 112.The processing engine 112 may process information and/or data related tothe service request to perform one or more functions described in thepresent disclosure. For example, the processing engine 112 may determineone or more candidate service provider terminals in response to theservice request received from the service requestor terminal 130. Insome embodiments, the processing engine 112 may include one or moreprocessing engines (e.g., single-core processing engine(s) or multi-coreprocessor(s)). Merely by way of example, the processing engine 112 mayinclude a central processing unit (CPU), an application-specificintegrated circuit (ASIC), an application-specific instruction-setprocessor (ASIP), a graphics processing unit (GPU), a physics processingunit (PPU), a digital signal processor (DSP), a field programmable gatearray (FPGA), a programmable logic device (PLD), a controller, amicrocontroller unit, a reduced instruction-set computer (RISC), amicroprocessor, or the like, or any combination thereof.

The network 120 may facilitate exchange of information and/or data. Insome embodiments, one or more components in the on-demand service system100 (e.g., the server 110, the service requestor terminal 130, theservice provider terminal 140, the vehicle 150, the database 160, andthe navigation system 170) may send information and/or data to othercomponent(s) in the on-demand service system 100 via the network 120.For example, the server 110 may receive a service request from theservice requestor terminal 130 via the network 120. In some embodiments,the network 120 may be any type of wired or wireless network, orcombination thereof. Merely by way of example, the network 120 mayinclude a cable network, a wireline network, an optical fiber network, atele communications network, an intranet, an Internet, a local areanetwork (LAN), a wide area network (WAN), a wireless local area network(WLAN), a metropolitan area network (MAN), a wide area network (WAN), apublic telephone switched network (PSTN), a Bluetooth network, a ZigBeenetwork, a near field communication (NFC) network, or the like, or anycombination thereof. In some embodiments, the network 120 may includeone or more network access points. For example, the network 120 mayinclude wired or wireless network access points such as base stationsand/or internet exchange points 120-1, 120-2, . . . , through which oneor more components of the on-demand service system 100 may be connectedto the network 120 to exchange data and/or information.

In some embodiments, a passenger may be an owner of the servicerequestor terminal 130. In some embodiments, the owner of the servicerequestor terminal 130 may be someone other than the passenger. Forexample, an owner A of the service requestor terminal 130 may use theservice requestor terminal 130 to send a service request for a passengerB, or receive a service confirmation and/or information or instructionsfrom the server 110. In some embodiments, a service provider may be auser of the service provider terminal 140. In some embodiments, the userof the service provider terminal 140 may be someone other than theservice provider. For example, a user C of the service provider terminal140 may use the service provider terminal 140 to receive a servicerequest for a service provider D, and/or information or instructionsfrom the server 110. In some embodiments, “passenger” and “passengerterminal” may be used interchangeably, and “service provider” and“service provider terminal” may be used interchangeably. In someembodiments, the service provider terminal may be associated with one ormore service providers (e.g., a night-shift service provider, or aday-shift service provider).

In some embodiments, the service requestor terminal 130 may include amobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, abuilt-in device in a vehicle 130-4, or the like, or any combinationthereof. In some embodiments, the mobile device 130-1 may include asmart home device, a wearable device, a smart mobile device, a virtualreality device, an augmented reality device, or the like, or anycombination thereof. In some embodiments, the smart home device mayinclude a smart lighting device, a control device of an intelligentelectrical apparatus, a smart monitoring device, a smart television, asmart video camera, an interphone, or the like, or any combinationthereof. In some embodiments, the wearable device may include a smartbracelet, a smart footgear, a smart glass, a smart helmet, a smartwatch, smart clothing, a smart backpack, a smart accessory, or the like,or any combination thereof. In some embodiments, the smart mobile devicemay include a smartphone, a personal digital assistance (PDA), a gamingdevice, a navigation device, a point of sale (POS) device, or the like,or any combination thereof. In some embodiments, the virtual realitydevice and/or the augmented reality device may include a virtual realityhelmet, a virtual reality glass, a virtual reality patch, an augmentedreality helmet, an augmented reality glass, an augmented reality patch,or the like, or any combination thereof. For example, the virtualreality device and/or the augmented reality device may include a Google™Glass, an Oculus Rift, a HoloLens, a Gear VR, etc. In some embodiments,built-in device in the vehicle 130-4 may include an onboard computer, anonboard television, etc. In some embodiments, the service requestorterminal 130 may be a device with positioning technology for locatingthe position of the passenger and/or the service requestor terminal 130.

The service provider terminal 140 may include a plurality of serviceprovider terminals 140-1, 140-2, . . . , 140-n. In some embodiments, theservice provider terminal 140 may be similar to, or the same device asthe service requestor terminal 130. In some embodiments, the serviceprovider terminal 140 may be customized to be able to implement theonline on-demand transportation service. In some embodiments, theservice provider terminal 140 may be a device with positioningtechnology for locating the service provider, the service providerterminal 140, and/or a vehicle 150 associated with the service providerterminal 140. In some embodiments, the service requestor terminal 130and/or the service provider terminal 140 may communicate with otherpositioning device to determine the position of the passenger, theservice requestor terminal 130, the service provider, and/or the serviceprovider terminal 140. In some embodiments, the service requestorterminal 130 and/or the service provider terminal 140 may periodicallysend the positioning information to the server 110. In some embodiments,the service provider terminal 140 may also periodically send theavailability status to the server 110. The availability status mayindicate whether a vehicle 150 associated with the service providerterminal 140 is available to carry a passenger. For example, the servicerequestor terminal 130 and/or the service provider terminal 140 may sendthe positioning information and the availability status to the server110 every thirty minutes. As another example, the service requestorterminal 130 and/or the service provider terminal 140 may send thepositioning information and the availability status to the server 110each time the user logs into the mobile application associated with theonline on-demand transportation service.

In some embodiments, the service provider terminal 140 may correspond toone or more vehicles 150. The vehicles 150 may carry the passenger andtravel to the destination. The vehicles 150 may include a plurality ofvehicles 150-1, 150-2, . . . , 150-n. One vehicle may correspond to onetype of services (e.g., a taxi hailing service, a chauffeur service, anexpress car service, a carpool service, a bus service, a driver hireservice, and a shuttle service).

The database 160 may store data and/or instructions. In someembodiments, the database 160 may store data obtained from the servicerequestor terminal 130 and/or the service provider terminal 140. In someembodiments, the database 160 may store data and/or instructions thatthe server 110 may execute or use to perform exemplary methods describedin the present disclosure. In some embodiments, database 160 may includea mass storage, a removable storage, a volatile read-and-write memory, aread-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage may include a magnetic disk, an optical disk, asolid-state drives, etc. Exemplary removable storage may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, etc. Exemplary volatile read-and-write memory may includea random access memory (RAM). Exemplary RAM may include a dynamic RAM(DRAM), a double date rate synchronous dynamic RAM (DDR SDRAM), a staticRAM (SRAM), a thyristor RAM (T-RAM), and a zero-capacitor RAM (Z-RAM),etc. Exemplary ROM may include a mask ROM (MROM), a programmable ROM(PROM), an erasable programmable ROM (PEROM), an electrically erasableprogrammable ROM (EEPROM), a compact disk ROM (CD-ROM), and a digitalversatile disk ROM, etc. In some embodiments, the database 160 may beimplemented on a cloud platform. Merely by way of example, the cloudplatform may include a private cloud, a public cloud, a hybrid cloud, acommunity cloud, a distributed cloud, an inter-cloud, a multi-cloud, orthe like, or any combination thereof.

In some embodiments, the database 160 may be connected to the network120 to communicate with one or more components in the on-demand servicesystem 100 (e.g., the server 110, the service requestor terminal 130,the service provider terminal 140, etc.). One or more components in theon-demand service system 100 may access the data or instructions storedin the database 160 via the network 120. In some embodiments, thedatabase 160 may be directly connected to or communicate with one ormore components in the on-demand service system 100 (e.g., the server110, the service requestor terminal 130, the service provider terminal140, etc.). In some embodiments, the database 160 may be part of theserver 110.

The navigation system 170 may determine information associated with anobject, for example, one or more of the service requestor terminal 130,the service provider terminal 140, the vehicle 150, etc. In someembodiments, the navigation system 170 may be a global positioningsystem (GPS), a global navigation satellite system (GLONASS), a compassnavigation system (COMPASS), a BeiDou navigation satellite system, aGalileo positioning system, a quasi-zenith satellite system (QZSS), etc.The information may include a location, an elevation, a velocity, or anacceleration of the object, or a current time. The navigation system 170may include one or more satellites, for example, a satellite 170-1, asatellite 170-2, and a satellite 170-3. The satellites 170-1 through170-3 may determine the information mentioned above independently orjointly. The satellite navigation system 170 may send the informationmentioned above to the network 120, the service requestor terminal 130,the service provider terminal 140, or the vehicle 150 via wirelessconnections.

In some embodiments, one or more components in the on-demand servicesystem 100 (e.g., the server 110, the service requestor terminal 130,the service provider terminal 140, etc.) may have permissions to accessthe database 160. In some embodiments, one or more components in theon-demand service system 100 may read and/or modify information relatedto the passenger, service provider, and/or the public when one or moreconditions are met. For example, the server 110 may read and/or modifyone or more passengers' information after a service is completed. Asanother example, the server 110 may read and/or modify one or moreservice providers' information after a service is completed.

In some embodiments, information exchanging of one or more components inthe on-demand service system 100 may be initiated by way of requesting aservice. The object of the service request may be any product. In someembodiments, the product may include food, medicine, commodity, chemicalproduct, electrical appliance, clothing, car, housing, luxury, or thelike, or any combination thereof. In some other embodiments, the productmay include a servicing product, a financial product, a knowledgeproduct, an internet product, or the like, or any combination thereof.The internet product may include an individual host product, a webproduct, a mobile internet product, a commercial host product, anembedded product, or the like, or any combination thereof. The mobileinternet product may be used in a software of a mobile terminal, aprogram, a system, or the like, or any combination thereof. The mobileterminal may include a tablet computer, a laptop computer, a mobilephone, a personal digital assistance (PDA), a smart watch, a point ofsale (POS) device, an onboard computer, an onboard television, awearable device, or the like, or any combination thereof. For example,the product may be any software and/or application used in the computeror mobile phone. The software and/or application may relate tosocializing, shopping, transporting, entertainment, learning,investment, or the like, or any combination thereof. In someembodiments, the software and/or application related to transporting mayinclude a traveling software and/or application, a vehicle schedulingsoftware and/or application, a mapping software and/or application, etc.In the vehicle scheduling software and/or application, the vehicle mayinclude a horse, a carriage, a rickshaw (e.g., a wheelbarrow, a bike, atricycle, etc.), a car (e.g., a taxi, a bus, a private car, etc.), atrain, a subway, a vessel, an aircraft (e.g., an airplane, a helicopter,a space shuttle, a rocket, a hot-air balloon, etc.), or the like, or anycombination thereof.

One of ordinary skill in the art would understand that when an elementof the on-demand service system 100 performs, the element may performthrough electrical signals and/or electromagnetic signals. For example,when a service requestor terminal 130 sends out a service request to theserver 110, a processor of the service requestor terminal 130 maygenerate an electrical signal encoding the request. The processor of theservice requestor terminal 130 may then send the electrical signal to anoutput port. If the service requestor terminal 130 communicates with theserver 110 via a wired network, the output port may be physicallyconnected to a cable, which further transmit the electrical signal to aninput port of the server 110. If the service requestor terminal 130communicates with the server 110 via a wireless network, the output portof the service requestor terminal 130 may be one or more antennas, whichconvert the electrical signal to electromagnetic signal. Similarly, aservice provider terminal 130 may receive an instruction and/or servicerequest from the server 110 via electrical signal or electromagnetsignals. Within an electronic device, such as the service requestorterminal 130, the service provider terminal 140, and/or the server 110,when a processor thereof processes an instruction, sends out aninstruction, and/or performs an action, the instruction and/or action isconducted via electrical signals. For example, when the processorretrieves or saves data from a storage medium, it may send outelectrical signals to a read/write device of the storage medium, whichmay read or write structured data in the storage medium. The structureddata may be transmitted to the processor in the form of electricalsignals via a bus of the electronic device. Here, an electrical signalmay refer to one electrical signal, a series of electrical signals,and/or a plurality of discrete electrical signals.

FIG. 2 is a block diagram illustrating an exemplary processing engine112 according to some embodiments of the present disclosure. Theprocessing engine 112 may include an acquisition module 210, ananalyzing module 220, a determination module 230, and a predictionmodule 240. Each module may be a hardware circuit that is designed toperform the following actions, a set of instructions stored in one ormore storage media, and/or any combination of the hardware circuit andthe one or more storage media.

The acquisition module 210 may be configured to obtain road-related dataassociated with a route (or routes) from the online on-demand servicesystem 100. In some embodiments, the road-related data by be transmittedas electrical signal. For example, the road-related data may besubstituted by or equivalent to electrical signal or electrical signalencoding road-related data as described elsewhere in the presentdisclosure. The route may correspond to a transportation service orgoods delivery service. The transportation service may include a taxihailing service, chauffeur service, express car service, carpoolservice, bus service, short-term driver-renting service, shuttleservice, or the like, or any combination thereof. The route may begenerated by at least one user terminal or a server of an onlineon-demand transportation platform, such as the system 100. The at leastone user terminal may be associated with at least one service provider,such as a driver, or service requestor, such as a passenger. Forexample, after sending a request for a transportation service, apassenger terminal may generate or obtain from the platform a route in amap for the transportation service. In some embodiments, the route maybe recorded and/or stored in the online on-demand service system 100.For example, during a transportation service, the online on-demandservice system 100 may record any information related to the route viathe network 120 and store the information in the database 160 or anycomponent in the online on-demand service system 100.

In some embodiments, the acquisition module 210 may acquire road-relateddata associated with a plurality of routes in the on-demand servicesystem 100. For example, the acquisition module 210 may acquire theplurality of routes generated in the last 6 months in the on-demandservice platform for carpool service. In some embodiments, the pluralityof routes may further be divided into different clusters. For example,the routes may be divided based on types of vehicles, types of services,areas of the routes, or time interval of the routes. The types ofvehicles may include private cars, taxis, trucks, motorcycle, autonomousvehicles, electric cars, bicycle, or the like, or any combinationthereof. The types of services may include cargo services, postservices, food order services, bus services, hitchhiking services,special car services, express car services, vehicle rent services,driving services, chauffeur, or the like, or any combination thereof.The areas of the routes may be geographical locations and/oradministrative districts in a map that at least part of the routeslocate in. The time interval of the routes may be a time period that astarting time and/or an ending time of the route belongs to. Merely byway of example, one day (from 0 a.m. to 24 p.m.) may be divided into aplurality of time intervals. Merely by way of example, the timeintervals may include 0 a.m. to 2 a.m., 2 a.m. to 4 a.m., . . . , 23p.m. to 24 p.m. In some embodiments, the time intervals may bedetermined according to other information including, for example,vacation, workday, rush time, the weather, or the like, or anycombination thereof. When the starting time or the ending time of theroute belongs to one time interval (e.g., 8 a.m. to 10. a.m.), the routemay belong to this time interval (e.g., 8. a.m. to 10 a.m.)correspondingly. In some embodiments, one route may belong to one timeinterval. In some other embodiments, one route may belong to at leasttwo time intervals.

The analyzing module 220 may be configured to determine a global featurevector and a historical travel duration of a route.

A global feature vector may be a mathematical expression (e.g., avector) to describe characteristics of a route as a whole. For example,the route may include at least one road section. The global featurevector may include not only features of individual sections in theroute, but also features that reflects interactions between differentindividual sections.

In some embodiments, the global feature vector may be a vector with onesingle column or one single row. The global feature vector maycorrespond to an N-dimensional coordinate system. Each dimension may beassociated to one item or feature of the route. In some embodiments, theglobal feature vector may include a relationship between at least two ofthe at least one road section. In some embodiments, the item or featureof the global feature vector may exclude the route's interactionrelation with other route. For example, the global feature vector may bedetermined according to one single route. In some embodiments, the itemor feature of the global feature vector may include interactionrelations between different routes. For example, the global featurevector of a target route may be determined according to two or moreroutes (e.g., hundreds of routes, millions of routes, or billions ofrouts, etc.) of which the road condition may affect that of the targetroute. The global feature vector may be further analyzed by theanalyzing module 220 or be used to establish a training set including aplurality of training examples. The training set may be used to train orestablish a model in the determination module 230 by using, for example,a machine learning method.

In some embodiments, the global feature vector may include a pluralityof items or features (e.g., a few millions of items or features)including, for example, traffic status, driving distance, starting time,starting location, destination location, sequence of satellitepositioning sampling points, driving distance on a specified level ofroad, number of road sections, number of crossroads with traffic lights,number of crossroads without traffic lights, vehicle status, or thelike, or any combination thereof. The traffic status may include areal-time road speed, or an estimated road speed. The driving distancemay include total distance of the route, or distance in each of the roadsections. The starting time may include a time that a passenger has beenpicked up, or a time that a user (e.g., a driver) has receive or confirma service request. The starting location may be a location where apassenger was picked up, or a location for a passenger to wait a driver.The sequence of satellite positioning sampling points may be a sequenceincluding vehicle positions that may be determined by a satellite (e.g.,via a GPS system). The driving distance on a specified level of road maybe a distance that a vehicle drives on a specified level of road, suchas a highway, a local road, a first-class road, a second-class road, athird-class road etc. The number of road sections may be a total numberof the road sections in one route. The vehicle status may include anaccess status (e.g., availability to accept the service request.) to thetransportation service request, a response probability, a preference ofa driver, a vehicle type, a current number of passengers in the vehicle,a maximum passenger capacity of the vehicle, a color of the vehicle, aservice level of the driver, a current speed of the vehicle, or anyinformation relative to the vehicle, or the like, or any combinationthereof.

In some embodiments, the global feature vector may be determined basedon, or at least in part, information from another system (e.g., aweather forecasting system, a traffic guidance system, or a trafficradio system, etc.). The off-line information may include weathercondition, traffic accident information, traffic congestion condition,traffic restriction, or any information related to the route. Theweather condition may include real-time weather information,substantially real-time weather information, weather forecastinformation, etc. The processing engine 112 may obtain the informationfrom the database 160, a weather condition platform (e.g., a weatherforecast website), a traffic guidance platform, and/or any other devicethat can provide these information, to determine the global featurevector. For example, the global feature vector may be determined basedon the data associated with the route and the weather condition relatedto the route.

In some embodiments, the historical travel duration of a route (e.g.,“historical duration”) may be the historical travel records occurred onthe route, e.g., the recorded overall time of each vehicle that drovethrough the route (from a starting location to an ending location ordestination). In some embodiments, the overall time may further include,for example, walking time, cycling time, riding time, driving time,flight time, shipping time, or the like, or any combination thereof.

In some embodiments, the analyzing module 220 may generate and savestructure data of global feature vector and historical durationassociated with the route. The structured data may be constructed orretrieved based on a B-tree, or a hash table.

The determination module 230 may be configured to determine structureddata of a model of estimated time of arrival (ETA) based on the globalfeature vector and the historical duration. The model of ETA may be usedto predict an ETA. In some embodiments, the model of ETA may be storedas an application, which may be used in a user terminal (e.g., a driverterminal) or an online platform (e.g., a website). For example, themodel of ETA may be send to a smartphone that may be used as a driverterminal for the transportation service, and the driver may log in theapplication for predicting the ETA from one location to anotherlocation. As another example, the model may be stored in the onlineon-demand service system 100 (e.g., the database 160), and a passengermay download or use the model of ETA on line via the network 120 or awired connection. In some embodiments, the model of ETA may be stored ina storage medium. For example, the model of ETA may be stored in anon-transitory computer-readable storage medium (e.g., a universalserial bus flash disk), which may be used by online on-demand servicesystem 100 or a user terminal (e.g., a passenger terminal).

In some embodiments, the model of ETA may be installed in another systemin order to predict the ETA. For example, the model of ETA may beinstalled in a video game system for car racing to predict the ETA. Asanother example, the model of ETA may be applied in a toy carcontrolling system in a gamepad for determining the ETA of the toy car.As still another example, the model of ETA may further be used in adriving simulator to train a racing driver or an astronaut in thedriving simulator. As still another example, the model of ETA may beused in a public cloud, and a user that may an access to the publiccloud may use the model of ETA.

The prediction module 240 may be configured to determine an ETA for asecond electrical signal encoding road-related data associated with anintended route having at least one road section. The ETA for theintended route may be used to definite a time duration from a startinglocation to an ending location of the intended route. For example, theETA for the intended route between the starting location and the endinglocation may be predicted as 5 minutes, 2 hours, or 1 day, etc.

In some embodiments, the ETA may be determined dynamically. For example,the ETA may be dynamically adjusted based on a weather condition (e.g.,a haze or a thundershower) or traffic accident information. As anotherexample, the ETA may be dynamically adjusted when a vehicle changes itsdriving modes. For example, a vehicle may include at least two modes ofno automation mode, driver assistance mode, partial automation mode,conditional automation mode, high automation mode, and full automationmode. The ETA may be dynamically adjusted when the vehicle switch fromone of these modes to another.

The modules in the processing engine 112 may be connected to orcommunicate with each other via a wired connection or a wirelessconnection. The wired connection may include a metal cable, an opticalcable, a hybrid cable, or the like, or any combination thereof. Thewireless connection may include a Local Area Network (LAN), a Wide AreaNetwork (WAN), a Bluetooth, a ZigBee, a Near Field Communication (NFC),or the like, or any combination thereof. In some embodiments, any two ofthe modules may be combined as a single module, and any one of themodules may be divided into two or more units.

FIG. 3 is a flowchart illustrating an exemplary process and/or method300 for determining an ETA for a transportation service, according tosome embodiments of the present disclosure. The process and/or method300 may be executed by the online on-demand service system 100. Forexample, the process and/or method 300 may be implemented as a set ofinstructions (e.g., an application) stored in database 160. Theprocessing engine 112 may execute the set of instructions and mayaccordingly be directed to perform the process and/or method 300 in anonline on-demand service platform. The platform may be an Internet-basedplatform that connects on-demand service providers and requestorsthrough Internet.

In step 310, the processing engine 112 may obtain road-related dataassociated with the route. In some embodiments, the road-related datamay be encoded by the processing engine 112 using an electrical signal.The route may have at least one road section. The road sections may bedivided based on, or at least in part, levels of road, traffic lights,cities, provinces, countries, geographical conditions, trafficidentification that related to the road or the route, or the like, orany combination thereof. For example, the road sections may be dividedbased on the levels of road. The levels of road may include afirst-class highway, a second-class highway, a third-class highway,sections of local roads, or the like, or any combination thereof.

As another example, the road sections may be divided based on thetraffic lights and/or highway exits etc. Accordingly, a route mayinclude a first road section, a second road section, a third section, .. . , Nth road section. Tow road sections adjacent to each other may belinked with at least one traffic light.

As still another example, the road sections may be divided based on thegeographical conditions. For example, a river, a bridge, a railway, atoll station, or the like, or any combination thereof may be used todivide a road adjacent thereto into two road sections.

In some embodiments, data of a road section may be affected not only byanother road section in the same route, but also road section(s) fromanother route. For example, a road section in a route may be directly orindirectly related to another road section(s) from the same and/ordifferent route(s). For example, a first road-related data associatedwith one road section (e.g., a car crash occurred in a first roadsection of a first route) may affect or correlate with a secondroad-related data associated with another road section (e.g., thetraffic of a second road section of a second route). As another example,one single route may include two road sections which may be linked by atraffic light. A traffic accident in a first road section may affecttravel speed in a second road section, or a traffic jam (e.g., anaccident-caused traffic jam) in the first road section may spread to thesecond road section subsequently causing a traffic jam in the secondroad section. As still another example, if a highway includes twoopposite routes connected by a green barrier, a vehicle restriction inone road section in the first route may cause a traffic jam in anotherroad section in the second route opposite to the first route.

In some embodiments, the relationship between the road sections and/orroutes may be quantized by a function, a parameter, a value, athreshold, a vector, a weight, a constant, or the like, or anycombination thereof. Merely by way of example, two road sectionsadjacent to each other may have a relationship designated with a weightof 0.7˜0.9, and two road sections apart from each other may bedesignated with a weight of 0.1˜0.3.

In some embodiments, the processing engine 112 may obtain a plurality ofroutes according to a map. The map may include a navigation map, asatellite map, an administrative map, a road-based map, a subway-basedmap, a railway-based map, a terrain map, a highway map, a tourist map,or the like, or any combination thereof. In some embodiments, the mapmay be integrated, modified, or changed. For example, the navigationmap, the satellite map, and a road-based map may be integrated as themodified map. In some embodiments, the processing engine 112 may obtainthe plurality of routes according to the map corresponding to a city ora district. For example, the plurality of routes may be obtainedaccording to the map corresponding to New York or Long Island districtof New York. As another example, the processing engine 112 may obtainall routes during a time interval according to the map of the U.S.highway system.

In some embodiments, the routes may be affected directly or indirectlywith each other. For example, if a snowstorm hit the U.S. west coast,speeds of the plurality of routes in Washington State may be slow down,which may affect speeds of the plurality of routes in Idaho. As anotherexample, a newly built road may affect (e.g., increase trafficcondition) of a plurality of routes close to the newly built road.

In some embodiments, the road-related data associated with the route mayinclude request data, order data, transaction data, map data, data of atraffic system, weather-related data, user data, any data measured inthe route, or the like, or any combination thereof. In some embodiments,the road-related data may be obtained by quantization. For example, aname of a street may be quantized into a number based on a lookup tablethat collects names of streets and corresponding numbers. In someembodiments, the road-related data may be analyzed based on, or at leastin part, the route. For example, during a national holiday, a cityordinance may place traffic restrictions on ABC Avenue, oralternatively, the ABC Avenue may experience extremely heavy traffic.Accordingly, the processing engine 112 may determine to place a trafficrestriction to the ABC Avenue and obtain road-related data correspondingto the traffic restriction in the ABC Avenue. As another example, if theprocessing engine 112 obtain an ID number of a user (e.g., driver'slicense No. of a driver and/or social security No. of the driver), theprocessing engine 112 may obtain user data of the user from a database(e.g., a public security network database that may be established by theMinistry of Public Security) according to the ID number.

The electrical signal encoding road-related data associated with theroute may be generated by a driver terminal or a passenger terminal. Forexample, the electrical signal may be transmitted by a driver terminalimplemented by a mobile device 1100; and the electrical signal may bereceived by the processing engine 112. In some embodiments, theelectrical signal may be transmitted via a wired connection or a wiredconnection. For example, the processing engine 112 may obtain theelectrical signal from the database 160 via the network 120.

In step 320, the processing engine 112 may determine structured data ofa global feature vector and structured data of historical durationassociated with the route based on the road-related data. The structureddata of a global feature vector and/or the structured data of historicalduration may be constructed or retrieved by the processing engine 112based on a B-tree, or a hash table. In some embodiments, the structureddata may be stored or saved as a form of a data library in the database160. The global feature vector and the historical durations may be usedto generate a plurality of training samples. The plurality of trainingsamples may form a training set that may be used to discover potentiallypredictive relationships or establish a model for prediction.

In some embodiments, the global feature vector may include a pluralityof items or features including, for example, traffic status, drivingdistance, starting time, starting location, destination location,sequence of satellite positioning sampling points, driving distance on aspecified level of road, number of road sections, number of crossroadswith traffic lights, number of crossroads without traffic lights,vehicle status, or the like, or any combination thereof. The trafficstatus may include a real-time road speed, or an estimated road speed.The driving distance may include total distance of the route, ordistance in each of the road sections. The starting time may include atime that a passenger has been picked up, or a time that a user (e.g., adriver) has receive or confirm a service request. The starting locationmay be a location where a passenger was picked up, or a location for apassenger to wait a driver. The sequence of satellite positioningsampling points may be a sequence including vehicle positions. Thevehicle positions may be determined by a satellite (e.g., via a GPSsystem). The driving distance on a specified level of road may be adistance that a vehicle drives on a specified level of road, such as ahighway, a local road, a first-class road, a second-class road, athird-class road, etc. The number of road sections may be a total numberof the road sections in one route. The vehicle status may include anaccess status (e.g., availability to accept the service request.) to thetransportation service request, a response probability, a preference ofa driver, a vehicle type, a current number of passengers in the vehicle,a maximum passenger capacity of the vehicle, a color of the vehicle, aservice level of the driver, a current speed of the vehicle, or thelike, or any combination thereof.

In some embodiments, the global feature vector may include an overallfeature vector. The overall feature vector may be determined from theroad sections having a relationship with each other. In someembodiments, the overall feature vector may associate with at least oneroad section from the same and/or different route(s). For example, theoverall feature vector may be determined from the road sections in onesingle route. As another example, the overall feature vector may bedetermined from the road sections in two or more routes (e.g., twoadjacent routes).

In some embodiments, the overall feature vector may be determined basedon all road sections in an area or within a map (e.g., a navigationmap). For example, the navigation map be installed in a driver terminal.The navigation map may have, for example, 200 road sections, and theprocessing engine 112 may determine the overall feature vector from the200 road sections. In some embodiments, size of the area or the map maybe adjusted or modified by a driver, a passenger, or the processingengine 112. For example, the processing engine 112 may obtainroad-related data associated with 3500 road sections in anadministrative map of Beijing. As another example, the processing engine112 may select 300 road sections by narrowing the size of administrativemap of Beijing into an area of interest. As another example, theprocessing engine 112 may divide the area or the map into two or moresub-areas (e.g., 10 equal sub-areas). The road sections in each of thesub-areas may be used to determine the overall feature vector. In someembodiments, the processing engine 112 may determine two or more overallfeature vectors (e.g., 10 overall feature vectors) according to thesub-areas.

In some embodiments, the global feature vector may be expressed as avector with one column or one row. For example, the global featurevector may be a row vector expressed as a 1×N determinant (e.g., a 1×25determinant). As another example, the global feature vector may be acolumn vector that may be expressed as an N×1 determinant (e.g., a 200×1determinant). In some embodiments, the global feature vector maycorrespond to an N-dimensional coordinate system. The N-dimensionalcoordinate system may be associated with N items or features of theroutes. In some embodiments, one or more global feature vectors may beprocessed by the processing engine 112. For example, three globalfeatures vectors (e.g., three row vectors) may be integrated into a 3×Ndeterminant. As another example, N global feature vectors (e.g., N1×Nrow vectors) may be integrated into an N×N determinant.

For example, referring to FIG. 9, a route 900 is shown in a road-basedmap. The route 900 may include 10 road sections (e.g., a first roadsection, a second road section, . . . , tenth road section) and 9traffic lights ((e.g., a first traffic light, a second traffic light, .. . , a ninth traffic light). Two adjacent road sections are linked withone or more traffic lights. Time of passing through each of the roadsections may be determined based on each of speeds in road sections. Forexample, the time of passing through the first road section may bedetermined as T1; the time of passing through the second road sectionmay be determined as T2; . . . ; and the time of passing through thetenth road section may be determined as T10. Time of passing througheach of the traffic lights may be determined as L1, L2, . . . , and L9.The processing engine 112 may determine a global feature vectoraccording to the road sections in the route 900. The global featurevector may include road-related data associated with all road sectionsin the map, for example, the global feature vector may include theroad-related data from the first road section to the twenty-seven roadsection.

The road-related data associated with each road section may includerequest data, order data, transaction data, map data, data of a trafficsystem, weather-related data, user data, or the like, or any combinationthereof. The global feature vector may further include data representingrelationships of road sections in the map. Relationships of roadsections may include relationships between any two road sections in themap, or relationships among any three or more road sections in the map.Number of relationships between any two road sections may be a2-combination of 27. Data representing relationships of road sections inthe map may be expressed as a function, a parameter, a value, athreshold, a vector, a weight, a constant, or the like, or anycombination thereof. Data representing relationships of road sectionsmay be determined by a formula (e.g., a Pearson product-momentcorrelation coefficient). In some embodiments, data representingrelationships of road sections may be generated based on a matrix with Mcolumns and M rows, where M may be any integer number, representingtwenty-seven road section in the map. The matrix may be sparse describedas, for example:

$\begin{matrix}{{{\begin{matrix}1.0 & 0.0 & 0.1 & \ldots & 0.1 \\0.2 & 1.0 & 0.0 & \ldots & 0.0 \\0.0 & 0.3 & 1.0 & \ldots & 0.0 \\\ldots & \ldots & \ldots & \ldots & \ldots \\0.0 & 0.0 & 0.0 & \ldots & 1.0\end{matrix}}},} & (1)\end{matrix}$

where values in a diagonal line position, for example, the second columnand the second row, may be 1. This may represent the second road sectionhas 100% correlation with itself. Values in the first column and thesecond row may be 0.2, this may represent the second road section has20% interaction relation with the first road section. Most of values inthe matrix may be 0, this may represent the corresponding two roadsections are uncorrelated.

In step 330, the processing engine 112 may determine a model ofestimated time of arrival (ETA) based on the global feature vector andthe historical duration.

In some embodiments, the processing engine 112 may use a machinelearning method to determine the model of ETA. In some embodiments, themachine learning method may include a decision tree algorithm, anassociation rule algorithm, an artificial neural networks algorithm, adeep learning algorithm, an inductive logic programming algorithm, asupport vector machines algorithm, a clustering algorithm, a Bayesiannetworks algorithm, a reinforcement learning algorithm, a representationlearning algorithm, a similarity and metric learning algorithm, a sparsedictionary learning algorithm, a genetic algorithms, a rule-basedmachine learning algorithm, or the like, or any combination thereof.

In some embodiments, the processing engine 112 may use a decision treealgorithm for determining a model of ETA. The decision tree algorithmmay include an iterative dichotomiser 3 algorithm, classification andregression tree algorithm, successor of ID3 algorithm, CHi-squaredautomatic interaction detector algorithm, conditional inference treesalgorithm, or the like, or any combination thereof.

In some embodiments, the model of ETA may be saved in a storage mediumin forms as structured data. The structured data of a model of ETA maybe constructed or retrieved by the processing engine 112 based on aB-tree, or a hash table. In some embodiments, the structured data may bestored or saved as a form of a data library in the database 160.

In step 340, the processing engine 112 may save the structured data ofthe model of ETA in a non-transitory computer-readable storage medium.In some embodiments, the processing engine 112 may send the structureddata of the model of ETA to an electronic device to conduct an ETA foran intended route. The non-transitory computer-readable storage mediummay include a universal serial bus flash disk, a hard disk, a solidstate disk, a CD ROM, and/or any computer-readable transitory ornon-transitory memory. For example, the processing engine 112 may savethe structured data of the model of ETA in the hard disk; and a driverterminal may read the hard disk and obtain the structured data of themodel of ETA.

The model of ETA may be stored as structured data and implemented as aset of instructions of an application and be installed in the electronicdevice. The electronic device may include smartphone, personal digitalassistant (PDA), tablet computer, laptop, carputer (board computer),play station portable (PSP), smart glasses, smart watch, wearabledevice, virtual display device, or display enhanced equipment (e.g.Google™ Glass, Oculus Rift, HoloLens, Gear VR, etc.), or the like, orany combination thereof. In some embodiments, the electronic device maybe a component of the online on-demand service system 100 (e.g., thedatabase 160). The electronic device may obtain the model of ETA bydownloading from any component in the online on-demand service system100 via the network 120. In some embodiments, the electronic device mayobtain the model of ETA from a non-transitory computer-readable storagemedium (e.g., a universal serial bus flash disk, a hard disk, a solidstate disk, a CD ROM, and/or any computer-readable transitory ornon-transitory memory, etc.).

FIG. 4 is a flowchart illustrating an exemplary process and/or method400 for determining an ETA for an intended route according to someembodiments of the present disclosure. The process and/or method 400 maybe executed by the online on-demand service system 100. For example, theprocess and/or method 400 may be implemented as a set of instructions(e.g., an application) stored in database 160. The processing engine 112may execute the set of instructions and may accordingly be directed toperform the process and/or method 400 in an online on-demand serviceplatform via receiving and/or sending electrical signals. The platformmay be an Internet-based platform that connects on-demand serviceproviders and requestors through Internet.

In step 410, the processing engine 112 may obtain a route having atleast one road section.

In some embodiments, the processing engine 112 may select a road-basedmap (e.g., a road-based map of Beijing), and obtain the route associatedwith the road-based map. The route may be recorded by the onlineon-demand service system 100 during a period (e.g., last three months orlast half year). For example, a driver may obtain a service request fromthe online on-demand service system 100. The service request may betransmitted by a passenger using a passenger terminal. After the servicerequest being confirmed by the driver, the online on-demand servicesystem 100 may start to record road-related data associated with a routethat correspond to the service request. In some embodiments, theprocessing engine 112 may record millions of routes or billions ofroutes during the period according to the road-based map of Beijing. Insome embodiments, any two of routes may be related to each other. Forexample, an accident in 5th Avenue of New York may block the trafficthereon. To avoid the traffic on the 5th Avenue, an increasing number ofdrivers may turn from the 5th Avenue to 138th Avenue of New York. As alarge number of vehicles run between 5th Avenue and 138th Avenue, routesin all avenues between 5th Avenue and 138th Avenue may come into a heavytraffic status (e.g., a slow speed). In some embodiments, the route mayinclude at least one road section. The plurality road sections mayinclude road sections between two traffic lights, road sections betweendifferent levels of roads, road sections linked by a bridge, roadsections separated by a river, road sections divided by a railway track,road sections with a fixed distance (e.g., one mile, or two kilometers),road sections among different administrative regions (e.g., differentcities, different provinces, or different countries), road sectionscorresponding to each of pickups of passengers, any road sections withthe same or different road properties, or the like, or any combinationthereof. For example, the route may include at least one road section(e.g., 50 road sections) with similar road properties. For example, theroute may include a section between two traffic lights. The roadconditions of the section, such as road width, speed limit, whethercondition, etc. may be the same throughout this section. Thus, thesection of the route may be described as a single section of the route.

The at least one road section of the route may be directly or indirectlyrelated to each other. For example, if a first road section is linkedwith a second road section, the first road section may have arelationship with the second road section. Speeds of in the first roadsection may affect or correlate with the speeds in the second roadsection. As another example, a road section in a first route may berelated to a road section in a second route. If the road section in afirst route is close to or near the road section in a second route, atraffic accident in the road section in a first route may affect speedsin the road section in a second route. As still another example, if alarge-scale activity (e.g., the Olympic Games or a World Exposition) ishosted in a district or a city, heavy traffic of plurality of roadsections in the district or the city may affect speeds of road sectionsin a nearby district or a nearby city.

In some embodiments, the relationship between the road sections and/orroutes may be quantized by a function, a parameter, a value, athreshold, a vector, a weight, a constant, or the like, or anycombination thereof. Merely by way of example, two road sectionsadjacent to each other may have a relationship designated with a weightof 0.7˜0.9, and two road sections apart from each other may bedesignated with a weight of 0.1˜0.3.

In step 420, the processing engine 112 may obtain road-related databased on the route having plurality of road sections. In someembodiments, the road-related data may be encoded by the processingengine 112 using an electrical signal.

The processing engine 112 may obtain the road-related data from thedatabase 160 or any component in the online on-demand service system100. In some embodiments, the road-related data may be obtained fromuser terminals (e.g., service requestor terminal 130 or service providerterminal 140). For example, the processing engine 112 may obtain theroad-related data from driver terminal or passenger terminal byanalyzing requests, service requests, transactions, navigationinformation, electronic map in user terminal, or the like, or anycombination thereof.

In step 430, the processing engine 112 may determine a global featurevector based on the electrical signal encoding road-related data. Insome embodiments, the processing engine 112 may use the road-relateddata to construct a data library, a data sheet, a matrix, a sequence, aset, any expression that may organize data, or the like, or anycombination thereof. For example, if road-related data is obtained fromdriver terminals, the processing engine 112 may use the road-relateddata to generate a sparse matrix including, for example, N columns and Nrows. In some embodiments, the sparse matrix may include items orfeatures including, for example, starting location, destination,starting time, waiting time, transaction time, gender, customer comment,driving experience, or the like, or any combination thereof. Theprocessing engine 112 may select one or more items or features from thesparse matrix to determine the global feature vector.

In some embodiments, the processing engine 112 may determine an overallfeature vector based on the road-related data. For example, theroad-related data associated with all road sections within a navigationmap of the driver terminal may be used to determine the overall featurevector. As another example, the road-related data may be associated withat least one road section that may have a relationship with each other.The road-related data associated with the related road sections may beused to determine the overall feature vector. The method or process todetermine an overall feature vector may be described as elsewhereaccording to the present disclosure.

In step 440, the processing engine 112 may determine a historicalduration based on the road-related data. The processing engine 112 maydirectly obtain the historical duration from the road-related data. Forexample, the processing engine 112 may obtain the historical durationfrom the database 160. As another example, the processing engine 112 maydetermine the historical duration by calculating time between a startingtime and an ending time. In some embodiments, the historical durationmay be corresponding to the global feature vector, and each of globalfeature vector and its corresponding historical duration may bedetermined as a training sample during a machine learning. For example,each of global feature vector may be determined as an input, and itscorresponding historical may be determined as a label during the machinelearning. As another example, millions of global feature vectors andtheir corresponding millions of historical duration may be used to traina model of ETA.

In some embodiments, the global feature vector and the historicalduration may be of a form of structured data. The structured data of aglobal feature vector and/or the structured data of historical durationmay be constructed or retrieved by the processing engine 112 based on aB-tree, or a hash table. In some embodiments, the structured data may bestored or saved as a form of a data library in the database 160.

In step 450, the processing engine 112 may train a model of estimatedtime of arrival (ETA) based on the global feature vector and thehistorical duration. In some embodiments, millions of global featurevectors and millions of historical duration may be used to train a modelof ETA. The Processing engine 112 may be used to a programming languageto implement process of training the model of ETA. The programminglanguage may include C programming language, Java programming language,PHP programming language, JavaScript programming language, C++programming language, Python programming language, Shell programminglanguage, Ruby programming language, Objective-C programming language,C# programming language, or the like, or any combination thereof. Insome embodiments, the process of training the model of ETA may beimplemented by a computer software including a matrix laboratory(MATLAB), a RapidMiner, an Orange, an open source computer visionlibrary (OpenCV), a statistical product and service solutions (SPAA), astatistical analysis system (SAS), or the like, or any combinationthereof. Each of the computer software (e.g., MATLAB) may includeplurality algorithms for training a model.

In some embodiments, the model of ETA may be of a form of structureddata. The structured data of a model of ETA may be constructed orretrieved by the processing engine 112 based on a B-tree, or a hashtable. In some embodiments, the structured data may be stored or savedas a form of a data library in the database 160.

In step 460, the processing engine 112 may obtain an intended route. Theintended route may be corresponding to a request that may be generatedby a passenger for a taxi or private car. The online on-demand servicesystem 100 may receive the request and determine the intended routebased on the content of the request (e.g., a starting location, astarting time, or a destination, etc.). In some embodiments, theintended route may be obtained from a driver terminal, when a driverconfirm or receive a service request from the online on-demand servicesystem 100. In some embodiments, the intended route may be obtained froma passenger terminal, when a passenger establish a request for atransportation service and confirm a service request transmitted fromthe online on-demand service system 100. In some embodiments, theprocessing engine 112 may obtain a plurality of intended routes that maycorrespond to a map (e.g., a road-based map of Beijing), and theplurality of intended routes may be directly or indirectly related toeach other. In some embodiments, the intended route may include at leastone road section as described elsewhere in the present disclosure, forexample, in step 310 and/or step 410.

In step 470, the processing engine 112 may determine an intended globalfeature vector based on the intended route. In some embodiments, theintended global feature vector may have N dimensions, where the N maycorrespond to N items or features obtained from the intended route. Forexample, if the processing engine 112 may select 2000 items or featuresfrom the intended route, a vector including 2000 columns or 2000 rowsmay be determined as an intended global feature vector for the intendedroute.

In some embodiments, dimensions of the intended global feature vectormay be less than that of the global feature vector. If one or more itemsor features in the intended route is missed, the dimension of theintended global feature vector corresponding to the intended route maydecrease, and the intended global feature vector with deceased dimension(e.g., a vector with R columns or R rows, where R may be less than N).In some embodiments, if one or more items or features in the intendedroute are missed, the processing engine 112 may determine an intendedglobal feature vector still with N columns or N rows; and columns orrows corresponding to the missing items may be described by a defaultvalue (e.g., a null).

In step 480, the processing engine 112 may determining an ETA for anintended route based on the model of ETA and the intended global featurevector. The processing engine 112 may determine the intended globalfeature vector as an input for the model of ETA; and the model of ETAmay come into an output according to the input. For example, if a driversend a request for a taxi to the online on-demand service system 100, anintended route with one or more road sections may be determined. Theprocessing engine 112 may determine the intended global feature vectorwith N dimensions according to the intended route; and the processingengine 112 may further determine the ETA for the intended route byinputting the intended global feature vector into the model of ETA. Insome embodiments, the step 480 may be implemented in an electronicdevice such a smartphone, a personal digital assistant (PDA), a tabletcomputer, a laptop, a carputer (board computer), a play station portable(PSP), a smart glasses, a smart watch, a wearable devices, a virtualdisplay device, display enhanced equipment (e.g. a Google™ Glass, anOculus Rift, a HoloLens, or a Gear VR), or the like, or any combinationthereof.

FIG. 5 is a flowchart illustrating an exemplary process and/or method500 for determining an ETA for an intended route based on a sub-model ofETA and a scenario of the intended route according to some embodimentsof the present disclosure. The process and/or method 500 may be executedby the online on-demand service system 100. For example, the processand/or method 500 may be implemented as a set of instructions (e.g., anapplication) stored in database 160. The processing engine 112 mayexecute the set of instructions and may accordingly be directed toperform the process and/or method 500 in an online on-demand serviceplatform. The platform may be an Internet-based platform that connectson-demand service providers and requestors through Internet.

In step 510, the processing engine 112 may obtain a route having atleast one road section. More descriptions regarding to the route may befound elsewhere in the present disclosure, for example, in step 310and/or step 410.

In step 520, the processing engine 112 may determine a scenario of theroute based on the route. The scenario may include a region of theroute, or a time interval of the route. The region of the route may bedetermined based in a stating location of the route, a destination ofthe route, an administrative division the route passing by or passingthough, or the like, or any combination thereof. For example, if theprocessing engine 112 may divide a map in to a region (e.g., a crowdedregion, a central business district (CBD) region, a region around of arailway station, etc.), a driver associated with the route may pick up apassenger in a starting location; the starting location may be in theCBD region; and the processing engine 112 may determine the scenario ofthe route as the CBD region. In some embodiments, the processing engine112 may collect the route with the same or similar scenario (e.g., inthe region around of a railway station). For example, the processingengine 112 may obtain routes in the CBD region in past 3 days. Asanother example, the processing engine 112 may collect routes during arush time (e.g. 7 a.m. to 9 a.m.).

In step 530, the processing engine 112 may obtain road-related databased on the route with at least one road section. In some embodiments,the road-related data may be encoded by the processing engine 112 usingan electrical signal. The processing engine 112 may obtain theroad-related data from a user terminal (e.g., a driver terminal, or apassenger terminal) or any component in the online on-demand servicesystem 100. For example, the processing engine 112 may receive theroad-related data associated with the routes having the same or similarscenario (e.g., close to Beijing West Railway Station) from driverterminals. For example, the processing engine 112 may obtain theroad-related data associated with the routes in a rush time (e.g. 7 a.m.to 9 a.m.) from the database 160.

In step 540, the processing engine 112 may determine a global featurevector and a historical duration based on the road-related data. Theglobal feature vector associated with a route may correspond to thehistorical duration associated with the same route; each global featurevector in a scenario (e.g., in a CBD region) and correspondinghistorical duration may be determined as a training sample; and thetraining sample may be used to form a training set that may be used todetermine a sub-model of estimated time of arrival. The training setassociated with the same or similar scenario may include hundreds of thetraining samples, or millions of the training samples.

In some embodiments, the global feature vector and the historicalduration may be of the form of structured data. The structured data of aglobal feature vector and/or the structured data of historical durationmay be constructed or retrieved by the processing engine 112 based on aB-tree, or a hash table. In some embodiments, the structured data may bestored or saved as a form of a data library in the database 160.

In step 550, the processing engine 112 may train a sub-model ofestimated time of arrival (ETA) based on the global feature vector andthe historical duration. The sub-model of ETA may be a model forpredicting an ETA for the intended route with the same scenario. Thesub-model of ETA may include a decision tree, an association rule, anartificial neural networks, a deep learning, an inductive logicprogramming, a support vector machines, a clustering, a Bayesiannetworks, a reinforcement learning, a representation learning, asimilarity and metric learning, a sparse dictionary learning, a geneticalgorithms, a rule-based machine learning, or the like, or anycombination thereof. In some embodiments, the sub-model of ETA may bedetermined by modifying or updating a model of ETA. In some embodiments,one or more sub-models of ETA may be used to synthesize a model of ETAor a new sub-model of ETA. In some embodiments, at least one sub-modelof ETA may be used to predict an ETA for an intended route. For example,a first sub-model of ETA may determine a first ETA for an intended routeand a second sub-model of ETA may determine a second ETA for the sameintended route. The processing engine 112 may determine a third ETA forthe intended route by calculating a mathematics average of the firstsub-model of ETA and the second sub-model of ETA. For example, theprocessing engine 112 may determine a synthesized sub-model of ETA basedon a first sub-model of ETA and a second sub-model of ETA. Thesynthesized sub-model of ETA may be used to determine an ETA.

In some embodiments, the sub-model of ETA may be of the form ofstructured data. The structured data of a sub-model of ETA may beconstructed or retrieved by the processing engine 112 based on a B-tree,or a hash table. In some embodiments, the structured data may be storedor saved as a form of a data library in the database 160.

In step 560, the processing engine 112 may obtain electrical signalencoding road-related data associated with an intended route having atleast one road section. The intended route may correspond to or belongto a scenario. For example, a passenger may sent a request for a privatecar to the online on-demand service system 100. The processing engine112 may determine the intended route based on the request for a privatecar; and the processing engine 112 may further determine the scenario ofthe intended route in step 570.

In some embodiments, the processing engine 112 may determine one or moreintended routes with same or different scenario. In some embodiments,the processing engine 112 may determine one or more scenarios for oneintended route. For example, if the intended route passed through a CBDregion during a rush time (e.g., 7 a.m. to 9 a.m.), the processingengine 112 may determine two scenarios that the intended route maybelong to.

In step 580, the processing engine 112 may determine an intended globalfeature vector based on the intended route. In some embodiments, theintended global feature vector may have N dimensions and may berepresent by a vector, where N may be any integer number. The sparsematrix may show N columns or N rows, where the N may correspond to Nitems or features obtained from the intended route. For example, if theprocessing engine 112 selects 50 items from the intended route, a vectorincluding 50 columns or 50 rows may be determined as an intended globalfeature vector for the intended route; and the intended global featurevector may be a vector with 50 dimensions.

In step 590, the processing engine 112 may determine an ETA for anintended route based on the sub-model of ETA, the intended globalfeature vector and the scenario of intended route. In some embodiments,a passenger may transmit a request for a taxi in rush time to the onlineon-demand service system 100. The processing engine 112 may determine anintended route and a scenario associated with the intended route; anintended global feature vector may be determined based on the intendedroute; the processing engine 112 may select a sub-model of ETA that maybe trained by a training set associated with a scenario (e.g., in rushtime), based on the scenario associated with the intended route; and theprocessing engine 112 may input the intended global feature vector tothe sub-model of ETA to generate an ETA for the intended route in rushtime.

In some embodiments, the processing engine 112 may send the ETA to adriver terminal, a passenger terminal, and/or any component of theonline on-demand service system 100 (e.g., the database 160). Thepassenger terminal may display in a user interface to facilitate thepassenger to determine whether to confirm a service request. In someembodiments, the ETA may be sent to a driver who may decide whether toselect the intended route for a service request (e.g., the driver maydrive a taxi according to another route different with the intendedroute).

FIG. 6 illustrates exemplary diagrams of a table associated with aglobal feature vector according to some embodiments of the presentdisclosure. The processing engine 112 may obtain road-related data froma driver terminal. In some embodiments, the road-related data may betransmitted as electrical signal. The road-related data may include textdata, numeral data, image data, video data, voice data, and/orcategorical data. The processing engine 112 may transform the text data,image data, video data, voice data, and categorical data into numeraldata. The processing engine 112 may further construct global featurevectors and historical durations based on the numeral data associatedwith the route. In some embodiments, the processing engine 112 maytransform the road-related data after global feature vectors andhistorical durations have been constructed.

For example, the processing engine 112 may collect road-related dataassociated with three routes that are denoted 3 route IDs: 1, 2, and 3,respectively (shown as in 610). Each of the road-related data associatedwith route IDs may include a driving distance 620, a number of trafficlight 630, a road width 640, a type of vehicle 650, a driving ranking660, and a historical duration 670. The processing engine 112 mayconstruct a global feature vector based on each of road-related data.Items or features in the global feature vector may respectively includethe driving distance 620, the number of traffic light 630, the roadwidth 640, the type of vehicle 650, and the driving ranking 660. Theprocessing engine 112 may further transform the text data and/orcategorical data (e.g., a taxi, or a private car, etc.) in the globalfeature vector into the numeral data. For example, the taxi and theprivate car may be transformed in to 1 and 2, respectively. In someembodiments, before training a model of ETA, the processing engine 112may transform the numeral data in the global feature vector into binarydata. The global feature vector may correspond to the historicalduration 670. For example, the historical duration may be used as labelto train a model. The global feature vector and corresponding historicalduration may form a training set. The processing engine 112 may train amodel of ETA by determining an input as the global feature vector anddetermining an output as the corresponding historical duration.

FIG. 7 illustrates exemplary diagrams of a decision tree as a model ofETA according to some embodiments of the present disclosure. Thedecision tree may include a classification tree, a regression tree, or aclassification and regression tree. Algorithm used in construct thedecision tree may include an iterative dichotomiser 3 algorithm,classification and regression tree algorithm, successor of ID3algorithm, CHi-squared automatic interaction detector algorithm, orconditional inference trees algorithm. During constructing the decisiontree 700, the processing engine 112 may use information gain todetermine which variable or item may be decided first in the decisiontree 700. The information gain may be expressed as formula (2):

Gain=info_(beforesplit)−infO_(aftersplit),  (2)

where the Gain represents the gain information; info_(beforesplit)represents entropy before splitting; and info_(aftersplit) representsentropy after splitting.

The entropy may be expressed as formula (3):

H(T)=−Σ_(i-1) ^(n) P _(i)·log₂ P _(i),  (3)

where H(T) represents entropy for variable T with n values; and P_(i)represents probability when T is i.

For example, the processing engine 112 may determine information gainfor each of variables (e.g., driving distance, number of traffic light,road width, and whether having picked up a passenger); the processingengine 112 may select a variable (e.g., the driving distance) with thelargest information gain; and the processing engine 112 may determinethe driving distance as a first split to contrast the decision tree 700.Other variables such as the number of traffic light, the road width, andwhether having picked up a passenger, may be split based oncorresponding information gain respectively. As shown in FIG. 7, thedecision tree 700 may be split first using the driving distance. If thedriving distance is equal to or greater than 10, the decision tree 700may be further split by the number of traffic light; if the number oftraffic light is equal to or greater than 1, the decision tree 700 maybe further split by whether having picked up a passenger; and if apassenger has been picked up, the ETA may be calculated by formula (4):

ETA=f ₁(X1)+f ₂(X2),  (4)

where the f₁ represents a function of the driving distance; and the f₂represents a function of the number of traffic light. If the passengerhasn't been picked up, the ETA may be calculated by formula (5):

ETA=f ₁(X1)+f ₂(X2)+30,  (5)

where the f₁ represents a function of the driving distance; and the f₂represents a function of the number of traffic light.

If the driving distance is equal to and greater than 0.5, and equal toand less than 10, the decision tree 700 may be further spit by the roadwidth, for example, if the road width is equal to 12, the ETA may becalculated by formula (6):

ETA=f1(X1)−X1/20,  (6)

where the f₁ represents a function of the driving distance. If the roadwidth is equal to 12, the ETA may be calculated by formula (7):

ETA=f ₁(X1)+X1/20,  (7)

where the f₁ represents a function of the driving distance.

If the driving distance is less than 0.5, the ETA may be 3 minutes. Insome embodiments, the decision tree 700 may output a number based on oneof the formula description. For example, the decision tree 700 mayoutput the ETA as 32 minutes based on the formula (7).

FIG. 8 illustrates exemplary diagrams of an artificial neural network(ANN) as model of ETA according to some embodiments of the presentdisclosure. The ANN 800 may include an input layer 810, a hidden layer820, and an output layer 830. The input layer 810 may consist of one ormore artificial neurons that may connect with each other to form a netstructure. As shown in FIG. 8, the input layer 810 may includeartificial neuron X1, artificial neuron X2, artificial neuron X3, . . ., artificial neuron Xn. In some embodiments, a number of the artificialneurons in the input layer 810 may correspond to the number of thecolumns of the matrix representing a global feature vector. For example,if the global feature vector corresponding to N items or features isexpressed as the vector with N columns or N rows, the number of theartificial neuron in the input layer 810 may be equal to N (e.g., 2000).The hidden layer 820 may include three artificial neurons, artificialneuron Z1, artificial neuron Z2, and artificial neuron Z3. Each of theartificial neurons in the hidden layer 820 may be connected with each ofthe artificial neurons in the input layer 810; and each of theartificial neurons in the hidden layer 820 may be connected withartificial neuron in the output layer 830. For example, the artificialneuron may connect with artificial neuron X1, artificial neuron X2,artificial neuron X3, . . . , and artificial neuron Xn, respectively.Each of the artificial neuron in the hidden layer 820 may include asummation function. The summation function may be a threshold functionor limiting function. For example, the summation function in the hiddenlayer may be expressed as formula (8):

O=f(Σ_(j=1) ^(n) w _(j) ·x _(j)−θ),  (8)

where O represents an output of artificial neuron to the artificialneuron in the output layer 830; f represents an activation function;w_(j) represents weight between the artificial neuron in the hiddenlayer 820; x_(j) represents input from artificial neuron Xj in the inputlayer to the artificial neuron in the hidden layer 820; and θ representsa threshold. The output layer 830 may include at least one artificialneuron. Each of the at least one artificial neuron in the output layer830 may connect with the each of the artificial neurons in the hiddenlayer 820. For example, one artificial neuron in the output layer 830may be connected with artificial neuron Z1, artificial neuron Z2, andartificial neuron Z3, respectively. Each of the artificial neuron in theoutput layer 830 may also include a summation function such as formula(8). For example, the activation function in the artificial neuron inthe output layer 830 may be a step function expressed in formula (9):

$\begin{matrix}{{f(x)} = \left\{ {\begin{matrix}{1,} & {{{{if}\mspace{14mu} x} \geq 0};} \\{0,} & {{{if}\mspace{14mu} x} < 0}\end{matrix},} \right.} & (9)\end{matrix}$

For an example, the activation function may be expressed as a functionwith a continuous function value (e.g., formula (4)). The artificialneuron with activation function in the output layer 830 may output theETA for a route or an intended route.

It should be noted that ANN 800 described in FIG. 8 is merely providedfor illustration purposes, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,various modifications and changes in the forms and details of theapplication of the above method and system may occur without departingfrom the principles in the present disclosure. For example, number ofthe hidden layer 820 may be two or more. As another example, ANN 800 mayfurther include a convolution layer and/or a pooling layer. Theconvolution layer and the pooling layer may be alternately arranged inthe hidden layer 820. As still another example, ANN 800 may be modifiedor trained by deep learning methods.

FIG. 9 illustrates exemplary diagrams of a physical model for predictingan ETA according to some embodiments of the present disclosure. Theprocessing engine 112 may obtain a route 900 (shown as in FIG. 9 withcontinuous line) described in a road-based map. The route 900 includes10 road sections (e.g., a first road section, a second road section, . .. , and tenth road section) and 9 traffic lights ((e.g., a first trafficlight, a second traffic light, . . . , and a ninth traffic light). Twoadjacent road sections are linked with one or more traffic lights. Timeof passing through each of the road sections may be determined based oneach of speeds in road sections. For example, the time of passingthrough the first road section may be determined as T1; the time ofpassing through the second road section may be determined as T2; . . . ;and the time of passing through the tenth road section may be determinedas T10. Time of passing through each of the traffic lights may bedetermined as L1, L2, . . . , and L9.

The processing engine 112 may determine the ETA for the route 900 byadding the times of passing through each of the road sections and thetimes of passing through each of the traffic lights. In someembodiments, the processing engine 112 may determine the ETA for theroute 900 based on the road sections in the route 900 and other roadsections in the road-based map. The road sections in the route 900 maybe directly or indirectly related to other road sections in theroad-based map. For example, a road section corresponding to T22 (shownas in FIG. 9 with dotted line) may have a relationship with the roadsections (e.g., the first road section) in the route 900; and a speed inthe road section corresponding to T22 may affect the speed in the firstroad section or any other road section in the route 900. In someembodiments, the processing engine 112 may determine a global featurevector based on the road sections in the route and other road sectionsin the road-based map. The model of ETA trained by the global featurevector may be used to predict the ETA for any route in the road-basedmap shown as in FIG. 9.

FIG. 10 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device 1000 on which the server 110,the service requestor terminal 130, and/or the service provider terminal140 may be implemented according to some embodiments of the presentdisclosure. For example, the processing engine 112 may be implemented onthe computing device 1000 and configured to perform functions of theprocessing engine 112 disclosed in this disclosure.

The computing device 1000 may be a general purpose computer or a specialpurpose computer, both may be used to implement an on-demand system forthe present disclosure. The computing device 1000 may be used toimplement any component of the on-demand service as described herein.For example, the processing engine 112 may be implemented on thecomputing device 1000, via its hardware, software program, firmware, orany combination thereof. Although only one such computer is shown, forconvenience, the computer functions relating to the on-demand service asdescribed herein may be implemented in a distributed fashion on a numberof similar platforms, to distribute the processing load.

The computing device 1000, for example, may include COM ports 1050connected to and from a network connected thereto to facilitate datacommunications. The computing device 1000 may also include a centralprocessing unit (CPU) 1020, in the form of one or more processors, forexecuting program instructions. The exemplary computer platform mayinclude an internal communication bus 1010, program storage and datastorage of different forms, for example, a disk 1070, and a read onlymemory (ROM) 1030, or a random access memory (RAM) 1040, for variousdata files to be processed and/or transmitted by the computer. Theexemplary computer platform may also include program instructions storedin the ROM 1030, RAM 1040, and/or other type of non-transitory storagemedium to be executed by the CPU 1020. The methods and/or processes ofthe present disclosure may be implemented as the program instructions.The computing device 1000 also includes an I/O component 1060,supporting input/output between the computer and other componentstherein such as user interface elements 1080. The computing device 1000may also receive programming and data via network communications.

Merely for illustration, only one CPU and/or processor is described inthe computing device 1000. However, it should be note that the computingdevice 1000 in the present disclosure may also include multiple CPUsand/or processors, thus operations and/or method steps that areperformed by one CPU and/or processor as described in the presentdisclosure may also be jointly or separately performed by the multipleCPUs and/or processors. For example, if in the present disclosure theCPU and/or processor of the computing device 1000 executes both step Aand step B, it should be understood that step A and step B may also beperformed by two different CPUs and/or processors jointly or separatelyin the computing device 1000 (e.g., the first processor executes step Aand the second processor executes step B, or the first and secondprocessors jointly execute steps A and B).

FIG. 11 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 1100 on which a useterminal may be implemented according to some embodiments of the presentdisclosure. As illustrated in FIG. 11, the mobile device 1100 mayinclude a communication platform 1110, a display 1120, a graphicprocessing unit (GPU) 1130, a central processing unit (CPU) 1140, an I/O1150, a memory 1160, and a storage 1190. In some embodiments, any othersuitable component, including but not limited to a system bus or acontroller (not shown), may also be included in the mobile device 1100.In some embodiments, a mobile operating system 1170 (e.g., iOS™,Android™, Windows Phone™, etc.) and one or more applications 1180 may beloaded into the memory 1160 from the storage 1190 in order to beexecuted by the CPU 1140. The applications 1180 may include a browser orany other suitable mobile apps for receiving and rendering informationrelating to image processing or other information from the processingengine 112. User interactions with the information stream may beachieved via the I/O 1150 and provided to the processing engine 112and/or other components of the online on-demand service system 100 viathe network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. A computer with user interface elements may be used to implementa personal computer (PC) or any other type of work station or terminaldevice. A computer may also act as a server if appropriately programmed.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python, or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

1. A system configured to operate an online on-demand transportationservice, comprising: a bus; logic circuits electronically connected toat least one storage medium via the bus, wherein during operation, thelogic circuits load a set of instructions from the at least one storagemedium and: obtain a first electrical signal from the bus, the firstelectrical signal being associated with at least one route having atleast one road section; generate and save first structured data of atleast one global feature vector and at least one historical durationassociated with the at least one route based on the first electricalsignal; generate second structured data of a model of estimated time ofarrival (ETA) by training the model based on the at least one globalfeature vector and the at least one historical duration; and save thesecond structured data of the model of ETA in the at least one storagemedium.
 2. The system of claim 1, wherein at least two of the at leastone global feature vector are integrated into a determinant.
 3. Thesystem of claim 2, wherein the at least one global feature vectorincludes an overall feature vector, wherein the overall feature vectorincludes a feature indicating a relationship between at least two of theat least one road section.
 4. The system of claim 3, wherein the atleast two of the at least one road section are apart from each other. 5.The system of claim 3, wherein the overall feature vector includes atleast one of traffic status, the traffic status including a real-timeroad speed or an estimated road speed.
 6. The system of claim 1, whereinthe model of ETA includes at least one of a regression model, a decisiontree model, an artificial neural network model, a support vectorregression model, a restricted Boltzmann Machine model, or a geneticalgorithm.
 7. The system of claim 1, wherein the model of ETA includes aplurality of sub-models of ETA, the plurality of sub-models of ETAcorresponding to at least one of time interval or region in the map. 8.The system of claim 1, wherein the logic circuits further: receive asecond electrical signal from the bus, the second electrical signalbeing associated with an intended route having at least one roadsection; generate and save third structured data of at least oneintended global feature vector associated with the intended route basedon the second electrical signal; and execute the second structured dataof the model of ETA to determine an ETA corresponding to the intendedroute, based on the intended feature vector and the model of ETA.
 9. Thesystem of claim 8, wherein to determine the ETA corresponding to theintended route, the logic circuits further: determine a time interval ofa starting time of the intended route; and select, based on the startingtime, the sub-model of ETA corresponding to time interval.
 10. Thesystem of claim 8, wherein to determine the ETA corresponding to theintended route, the logic circuits further: determine a region of astarting location or an ending location of the intended route; andselect, based on the starting location and the ending location, thesub-model of ETA corresponding to the region in the map.
 11. A methodconfigured to operate an online on-demand transportation service on atleast one electronic device having logic circuits, at least one storagemedium, and a communication platform connected to a network, comprising:obtaining, by the logic circuits, a first electrical signal associatedwith at least one route having at least one road section; generating andsaving, by the logic circuits, first structured data of at least oneglobal feature vector and at least one historical duration associatedwith the at least one route based on the first electrical signal;generating, by the logic circuits, second structured data of a model ofestimated time of arrival (ETA) by training the model based on the atleast one global feature vector and the at least one historicalduration; and saving, by the logic circuits, the second structured dataof the model of ETA in the at least one storage medium.
 12. The methodof claim 11, wherein at least two of the at least one global featurevector are integrated into a determinant.
 13. The method of claim 12,wherein the at least one global feature vector includes an overallfeature vector, wherein the overall feature vector includes a featureindicating a relationship between at least two of the at least one roadsection.
 14. The method of claim 13, wherein the at least two of the atleast one road section are apart from each other.
 15. The method ofclaim 13, wherein the overall feature vector includes at least one oftraffic status, the traffic status including a real-time road speed oran estimated road speed.
 16. The method of claim 11, wherein the modelof ETA includes at least one of a regression model, a decision treemodel, an artificial neural network model, a support vector regressionmodel, a restricted Boltzmann Machine model, or a genetic algorithm. 17.The method of claim 11, wherein the model of ETA includes a plurality ofsub-models of ETA, the plurality of sub-models of ETA corresponding toat least one of time interval or region in the map.
 18. The method ofclaim 11, further comprising: receiving, by the logic circuits, a secondelectrical signal associated with an intended route having at least oneroad section; generating and saving, by the logic circuits, thirdstructured data of at least one intended global feature vectorassociated with the intended route based on the second electricalsignal; and executing, by the logic circuits, the second structured dataof the model of ETA to determine an ETA corresponding to the intendedroute, based on the intended feature vector and the model of ETA. 19.The method of claim 18, wherein determining the ETA corresponding to theintended route comprising: determining, by the logic circuits, a timeinterval of a starting time of the intended route; and selecting, by thelogic circuits, based on the starting time, the sub-model of ETAcorresponding to time interval.
 20. A non-transitory computer-readablestorage medium including instructions that, when accessed by at leastone processor of an online on-demand service platform from a requestorterminal, causes the at least one processor to: obtain a firstelectrical signal associated with at least one route having at least oneroad section; generate and save first structured data of at least oneglobal feature vector and at least one historical duration associatedwith the at least one route based on the first electrical signal;generate second structured data of a model of estimated time of arrival(ETA) by training the model based on the at least one global featurevector and the at least one historical duration; and save the secondstructured data of the model of ETA in the non-transitorycomputer-readable storage medium.